.png)
A finance director at a mid-size manufacturing company built her annual budget over eight weeks in September. By mid-November, a key raw material supplier raised prices by 12%. By January, a competitor had entered two of her largest markets. By March, the leadership team was making resourcing decisions that bore no relationship to the plan she had spent two months building.
She was not incompetent. Her team was not lazy. The model she used — one built entirely on historical line items and incremental percentage assumptions — simply had no mechanism for responding when the inputs changed. Nobody had told the budget what drove it.
That is the structural problem driver-based planning is designed to solve.
According to the 2025 Deloitte CFO Signals Survey, 75% of CFOs report their traditional budgets are outdated within 90 days of approval. Not 90 days from the start of the year — 90 days from sign-off, which in many organisations happens weeks before the year even begins. A budget that is already stale before January 1 is not a planning tool. It is a historical document with a future date on the cover.
Driver-based planning addresses this at the structural level — not by making the annual budget faster to produce, but by replacing the assumptions underneath it with a model that updates automatically when the business changes.
Driver-based planning is a forecasting methodology where every line in a financial model is derived from one or more operational drivers — measurable business variables that directly cause financial outcomes to change.
The contrast with traditional budgeting is fundamental, not superficial.
In traditional budgeting, a revenue line might be set as "last year's revenue plus 8%." That number reflects a negotiated expectation, not a causal model. When actuals diverge from it, the organisation knows something went wrong but often cannot identify what — because the budget was not built from causes in the first place.
In a driver-based model, that same revenue line would be expressed as a formula:
Each of those inputs — active customers, contract value, renewal rate — is a driver. It is measurable, owned by someone in the business, and updatable when conditions change. When renewal rate drops from 87% to 81%, the forecast updates immediately. The impact flows through margin, headcount, and cash automatically. The FP&A team does not need to rebuild the model. They need to understand why renewal rate moved and what to do about it.
This is what the AFP's 2024 FP&A Guide to Driver-Based Models and Plans describes as the core value of the methodology: it shifts the planning conversation from "what happened" to "why it happened and which lever to pull next."
The case against traditional budgeting is not philosophical. It is structural, and it is made by the data.
According to the 2025 AFP Benchmarking Report, the average budget cycle for a mid-market company takes four to six weeks. During that window, the assumptions being locked into the model are based on conditions from weeks or months earlier. By the time the budget is approved, the market has moved. By the time it reaches operational leaders, it is already a lagging indicator dressed as a forward plan.
Gartner's late-2024 survey of 250 CFOs found that slower top-line growth was the number one challenge for 2025 — yet three quarters of finance respondents said they were more focused on downside risk and cost containment in their scenario planning. Those two facts sit in direct tension with traditional budgeting. A process that locks in assumptions once a year cannot simultaneously support downside scenario planning on a rolling basis.
The deeper problem is what FP&A Trends describes as the distinction between consolidation and modelling. Most reasonably sized organisations have systems that can consolidate actuals — add them up by division, currency, and P&L line. What they frequently lack is a live modelling layer underneath: the structure that shows how operational inputs cause financial outputs to move. Without that layer, scenario planning is not possible. You can describe what happened. You cannot credibly model what would happen if conditions changed.
Traditional budgeting institutionalises this gap. Driver-based planning closes it.
Driver-based planning works by identifying the smallest number of operational variables that explain the largest share of financial outcomes — and then connecting those variables directly to the financial model.
According to FP&A Trends research, the most common mistake organisations make when building driver-based models is selecting too many drivers. A model with 80 drivers becomes as opaque and unmaintainable as the line-item budget it replaced. The goal is a model that is accurate and actionable — not one that is technically comprehensive. The starting point is not the chart of accounts. It is the business.
A straightforward example from a B2B software company illustrates the principle:
Each of these drivers is owned by a function: sales owns new logo count and deal size, customer success owns upsell and renewal, infrastructure owns hosting cost per customer. When finance updates the model monthly, they are not rebuilding assumptions from scratch. They are asking each function owner to confirm or revise their driver, and the financial impact cascades automatically.
This is precisely what makes scenario planning tractable. Rather than building three entirely separate budgets for base case, upside, and downside, the team adjusts driver assumptions — new logo count drops 20%, deal size compresses 10% — and the P&L, balance sheet, and cash flow model instantly reflects the result. According to a McKinsey report, companies employing scenario analysis this way were 30% more likely to anticipate and mitigate risks effectively than those relying on static models.
Not all drivers are the same. Understanding the different types helps finance teams build models that are both accurate and maintainable.
The AFP's 2024 Guide to Driver-Based Models recommends that finance teams identify no more than 10 to 15 critical drivers across these categories as a starting point, validating each one against historical data before including it in the live model.
The methodology is sound. The implementation is where most organisations run into difficulty. According to FP&A Trends research on common pitfalls in driver-based forecasting, the failure points are predictable.
Five implementation steps:
Driver-based planning can be started in a structured spreadsheet environment. But the methodology reaches its full value when it runs on a platform where model logic is centralised, driver assumptions are transparent to all stakeholders, scenario analysis is automated, and actuals flow in directly from source systems.
EPM platforms including Anaplan, Jedox, and OneStream are specifically designed around this architecture. They allow driver assumptions to be owned and updated by the operational teams who know them, with the financial impact flowing through the model automatically. Finance teams gain a planning environment where the model does not need to be rebuilt each cycle — only the driver inputs need reviewing.
According to Gartner's 2024 research, 77% of CFOs and senior finance leaders plan to increase spending on FP&A technology in 2025, with nearly half expecting to raise it by 10% or more compared to the prior year. The organisations making that investment most effectively are not those buying platforms to run the same planning process faster. They are those redesigning their planning methodology — shifting to driver-based models — and then deploying the platform to scale it.
Keansa's FP&A practice builds driver-based planning models for mid-enterprise organisations across Anaplan, Jedox, and OneStream. The starting point is always methodology, not technology — defining the right drivers, validating them against historical data, and designing the model structure before any platform configuration begins.
The business case for driver-based planning is not primarily about forecast accuracy — though that improves significantly. According to Financial Models Lab research published in February 2026, companies that shift from traditional line-item budgets to driver-based models typically see forecasting accuracy improve by more than 25%.
The more significant benefit is what it does to finance's relationship with the rest of the business. When operational leaders understand that the financial plan is built from their inputs — their sales volume assumptions, their hiring plans, their unit cost estimates — they engage with the plan differently. They own their assumptions rather than treating the budget as something finance imposed on them.
According to a Gartner survey of 251 CFOs published in November 2024, metrics, analytics, and reporting were ranked as the number one focus area for 2025 — described as reflecting an emphasis on delivering insight to improve business performance. Driver-based planning is the structural foundation that makes that insight possible. Without a causal model connecting operational activity to financial outcomes, metrics and analytics describe what happened. With it, they explain why and forecast what comes next.
The 34% of CFOs who only collaborate seamlessly with their chief sales officer, according to Workday's 2024 research, face a coordination problem that driver-based planning directly addresses. A shared model with shared driver ownership creates the mechanism for cross-functional planning conversations — not as periodic events, but as a continuous working relationship.
Driver-based planning is not a technology. It is a way of thinking about what causes financial outcomes and building plans around those causes rather than around historical outcomes.
The shift from line-item budgets to driver-based models does not make planning simpler in the short term. It requires more rigour upfront — in driver identification, model design, and cross-functional engagement — than copying last year's numbers and adding a growth rate. But it produces a fundamentally different result: a plan that updates when the business changes, scenarios that can be run in hours rather than weeks, and a finance function that can explain not just what happened but what drove it and what to do next.
When 75% of CFOs report their budgets are outdated within 90 days of approval, the case for staying with the current approach is difficult to make. The case for driver-based planning is not theoretical. It is the practical response to a structural problem that most finance functions are already experiencing.
Related Resources
Keansa helps mid-enterprise finance teams design and implement driver-based planning models across Anaplan, Jedox, and OneStream — starting with driver identification and model design before any platform is deployed.
Book a Planning Assessment →.png){kind=small}
.png){kind=small}
.png){kind=small}